{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\" Note. Neither approach uses a state (caching), so the inference times are quad/linear, resp. \\nThey would be linear/constant if we kept a KV cache in Softmax or state in LinearAttn. \\nNonethless, this suffices to see the O(S) speedup during inference going softmax -> linear attention. \\nOur focus is on inference here, so we don't train/profile models with these, just fwd passes. \""
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "\n",
    "''' \n",
    "From \"Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention\"\n",
    "(https://arxiv.org/pdf/2006.16236)\n",
    "\n",
    "We follow notation from the paper, eg. the matrices 'o' and 'n' as well as the recurrent formulation. \n",
    "\n",
    "\n",
    "Note. Neither approach uses a state (caching), so the inference times are quad/linear, resp. \n",
    "They would be linear/constant if we kept a KV cache in Softmax or state in LinearAttn. \n",
    "Nonethless, this suffices to see the O(S) speedup during inference going softmax -> linear attention. \n",
    "Our focus is on inference here, so we don't train/profile models with these, just fwd passes. \n",
    "\n",
    "The TLDR here is this: removing softmax makes us able to switch some order of operations to compute \n",
    "(Q@K.T)V = Q(K.T@V) by associativity of matrix multiplication here. The critical thing, which was not obvious to me \n",
    "until I thought about it, is that the right matmul is over dim S so is O(SD^2) whereas the left matmul is \n",
    "O(S^2D) and since S>>D often, we go from quadratic to linear in sequence length. The phi notation is just \n",
    "a generalization, you can essentially think of it as just dropping the softmax that would violate \n",
    "associativity and thus make the above equality fail.\n",
    "\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# [Training] TIME: O(S^2), MEM: O(S) w/ FlashAttn and O(S^2) without\n",
    "    # Note. It's the online softmax trick that gives asymptotic gains in FlashAttn\n",
    "    # and obviously not any systems tricks, which (while important) only affect constant factors\n",
    "# [Inference] TIME: O(S), MEM: O(S) w/ KV cache and O(S^2) without\n",
    "class SoftmaxAttention(nn.Module): # single head for now \n",
    "    def __init__(self, D=256): \n",
    "        super().__init__()\n",
    "        self.D = D\n",
    "        self.scale = torch.sqrt(torch.tensor(D, dtype=torch.float32))\n",
    "        self.wq = nn.Linear(D, D)\n",
    "        self.wk = nn.Linear(D, D)\n",
    "        self.wv = nn.Linear(D, D)\n",
    "        self.wo = nn.Linear(D, D)\n",
    "\n",
    "    def forward(self, x): # x is [B, S, D]\n",
    "        Q, K, V = self.wq(x), self.wk(x), self.wv(x)\n",
    "        A_logits = (Q @ K.transpose(1, 2))/self.scale\n",
    "        A = F.softmax(A_logits, dim=-1) # [B, S, S], apply softmax on the last dimension\n",
    "        return self.wo(A@V) # A@V is [B, S, D]\n",
    "\n",
    "# [Training] TIME: O(S), MEM: O(S)\n",
    "# [Inference] TIME: O(1), MEM: O(1)\n",
    "class LinearAttention(nn.Module): \n",
    "    def __init__(self, D=256): \n",
    "        super().__init__()\n",
    "        self.D = D \n",
    "        self.wq = nn.Linear(D, D)\n",
    "        self.wk = nn.Linear(D, D)\n",
    "        self.wv = nn.Linear(D, D)\n",
    "        self.wo = nn.Linear(D, D)\n",
    "        self.phi = lambda x: F.elu(x) + 1\n",
    "\n",
    "    def forward(self, x): # x is [B, S, D]\n",
    "        # get Q, K, V projections\n",
    "        Q, K, V = self.wq(x), self.wk(x), self.wv(x)\n",
    "        \n",
    "        # apply feature map phi, see paper \n",
    "        Q_phi = self.phi(Q)  # [B, S, D]\n",
    "        K_phi = self.phi(K)  # [B, S, D]\n",
    "        \n",
    "        # kv_accum and norm_accum via einsum \n",
    "        kv_accum = torch.einsum('bsd,bse->bde', K_phi, V) # BSD @ BSD -> BDD\n",
    "        norm_accum = torch.sum(K_phi, dim=1) # [B, D]\n",
    "\n",
    "        # o via einsum \n",
    "        o = torch.einsum('bsd,bde->bse', Q_phi, kv_accum) # BSD @ BDD -> BSD\n",
    "        \n",
    "        # normalization factor - correct shape handling\n",
    "        norm_factor = torch.einsum('bsd,bd->bs', Q_phi, norm_accum).unsqueeze(-1) # [B, S, 1]\n",
    "        \n",
    "        return self.wo(o/norm_factor)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Benchmarking attention mechanisms (B=1):   0%|                                                                                              | 0/6 [00:00<?, ?it/s]"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Benchmarking attention mechanisms (B=1): 100%|██████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:00<00:00, 11.21it/s]\n",
      "Benchmarking attention mechanisms (B=64): 100%|█████████████████████████████████████████████████████████████████████████████████████| 6/6 [00:02<00:00,  2.86it/s]\n",
      "Benchmarking attention mechanisms (B=1024): 100%|███████████████████████████████████████████████████████████████████████████████████| 6/6 [00:03<00:00,  1.89it/s]\n"
     ]
    }
   ],
   "source": [
    "def benchmark_attention_mechanisms(batch_sizes=[1, 256], seq_lengths=[32, 128, 256, 512, 1024, 2048, 4096], device='cuda', num_runs=5):\n",
    "    results = {}\n",
    "    \n",
    "    for batch_size in batch_sizes:\n",
    "        results[batch_size] = {\n",
    "            'seq_lengths': seq_lengths,\n",
    "            'linear_times': [],\n",
    "            'softmax_times': [],\n",
    "            'linear_oom': [],\n",
    "            'softmax_oom': []\n",
    "        }\n",
    "        \n",
    "        # Initialize models\n",
    "        linear_attn = LinearAttention().to(device)\n",
    "        softmax_attn = SoftmaxAttention().to(device)\n",
    "        \n",
    "        for seq_len in tqdm(seq_lengths, desc=f\"Benchmarking attention mechanisms (B={batch_size})\"):\n",
    "            # Create random input tensor\n",
    "            x = torch.randn(batch_size, seq_len, 256, device=device)\n",
    "            \n",
    "            # Warm-up\n",
    "            try:\n",
    "                with torch.no_grad():\n",
    "                    linear_attn(x)\n",
    "            except torch.cuda.OutOfMemoryError:\n",
    "                pass\n",
    "                \n",
    "            try:\n",
    "                with torch.no_grad():\n",
    "                    softmax_attn(x)\n",
    "            except torch.cuda.OutOfMemoryError:\n",
    "                pass\n",
    "            \n",
    "            # Benchmark LinearAttention\n",
    "            torch.cuda.synchronize()\n",
    "            linear_times = []\n",
    "            linear_oom = False\n",
    "            try:\n",
    "                for _ in range(num_runs):\n",
    "                    start_time = time.time()\n",
    "                    with torch.no_grad():\n",
    "                        linear_attn(x)\n",
    "                    torch.cuda.synchronize()\n",
    "                    linear_times.append(time.time() - start_time)\n",
    "                avg_linear_time = np.mean(linear_times)\n",
    "                results[batch_size]['linear_times'].append(avg_linear_time)\n",
    "                results[batch_size]['linear_oom'].append(False)\n",
    "            except torch.cuda.OutOfMemoryError:\n",
    "                results[batch_size]['linear_times'].append(None)\n",
    "                results[batch_size]['linear_oom'].append(True)\n",
    "                torch.cuda.empty_cache()\n",
    "            \n",
    "            # Benchmark SoftmaxAttention\n",
    "            torch.cuda.synchronize()\n",
    "            softmax_times = []\n",
    "            softmax_oom = False\n",
    "            try:\n",
    "                for _ in range(num_runs):\n",
    "                    start_time = time.time()\n",
    "                    with torch.no_grad():\n",
    "                        softmax_attn(x)\n",
    "                    torch.cuda.synchronize()\n",
    "                    softmax_times.append(time.time() - start_time)\n",
    "                avg_softmax_time = np.mean(softmax_times)\n",
    "                results[batch_size]['softmax_times'].append(avg_softmax_time)\n",
    "                results[batch_size]['softmax_oom'].append(False)\n",
    "            except torch.cuda.OutOfMemoryError:\n",
    "                results[batch_size]['softmax_times'].append(None)\n",
    "                results[batch_size]['softmax_oom'].append(True)\n",
    "                torch.cuda.empty_cache()\n",
    "            \n",
    "    return results\n",
    "\n",
    "# different batch sizes/seqlens represent different kinds of workloads that need to be \n",
    "# accomodated in practice when serving many users \n",
    "batch_sizes = [1, 64, 1024]\n",
    "seqlens = [32, 128, 512, 2048, 8192, 16384]\n",
    "results = benchmark_attention_mechanisms(batch_sizes, seqlens)\n",
    "\n",
    "# run next cell to see plots from these results!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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rJEk5OTl65plnZBiG73MEqiYyB8oDuQNlRe5AqJE7EAxyR3DIHSgNcgfKA7kDZUXuQCiRORAsckfZkTkAnC4GZyKgn3/+WU8++aSkgjfRG2+80bevWbNm+v333zVlyhTt2bNH0qmXtna5XIqPj5fT6dTTTz/t66wgIFQfP/zwg15++WX9+eefvtnFkjRz5kwNHTpUt9xyi2JjY4tdOsZsNisuLk779u3TsmXL1LhxY98+b0h45plntHPnTvXp08d3yRhCQtVX+HXIZrPp+eefl2EYGjBgQJk6LLyhIT4+XvHx8YqKitKzzz5LR0U1Va9ePTVt2lR79uxR27ZttWHDBs2ZMydgp4WXyWRSSkqKzjrrLE2ZMkXPPPOMbDabunXrpmbNmmnv3r169913NXPmTNWuXVtPP/200tLSKrCWCKXp06fr0UcflSTddtttateunbKzs7Vx40Z99dVX+umnn7R8+XJt3rxZTz31lN/KCF7ez0k5OTmSTlzyLJI7TadOnaphw4ZJkq699lo1aNBAUVFReu2117Ry5UrNmDFDffv29Xu/tlgsSk1N1VlnnaWpU6fq9ddf1xdffCGp4LG66aabfMdeeumlioqK0hlnnKGmTZuGtW4IHTIHygO5A2VF7kB5IHegrMgdwSF3oDTIHSgP5A6UFbkDoUbmQDDIHWVH5gAQCgzORDGbNm3S22+/LbvdrkcffdTXWeH9AGcYhnJzcyWdWK46OztbqampvnN4j3W5XMrPz5fT6VReXp6eeuopZWRkRNybMk6u8IeW8847Tw0aNFBeXp62bdumjIwMvfbaa6pdu7auuOKKYrdNS0tTt27dNG3aNI0ZM0Y2m01du3ZVkyZNtG/fPr311lv68ccfVbt2bY0ZM4aQECG2bdumjz/+WJLUv39/7d27V+vXr9cLL7wgSWXqsPCGBIfDoby8PI0cOVI7duzgdaga8s5Ub968uX799Vf16dNHqampmj9//kk7Lby3u+2227Rr1y4tWbJEr776qtLS0hQTE6O8vDwdPnxY9erV0wcffKAmTZpUcE0RKr/99pueffZZSdKYMWN0/fXX++1v0aKFXn75ZR05ckSTJk3S4cOH9frrr8tqtfq1IW8o93ZSGIahUaNGRWRHhSTNmzfP975ftKOhefPmGjJkiObPn689e/aoTp06vs+XUsFjlZ6eLofDEbCzwntZL7PZrIEDB4azWggxMgfKA7kDZUXuQHkgd6CsyB3BIXegNMgdKA/kDpQVuQOhRuZAMMgdZUfmABAqDM6EH4fDoZ9//lmrV6/WNddcozvuuENSwZuD94322LFjOnr0qCwWi+Li4jRq1Cj99ttvatmypTp27KgbbrhB0dHRcrvdslqtslqtio6OlsViUUZGhpKSkri8RzUyd+5c34eWESNG6JZbbvHt27Bhg9544w3NmzdPb775prp3764GDRr49ntDwu233649e/ZoxYoVeumll5SWlqbY2Fjl5ubq4MGDqlu3rsaPH+83yxRVl8Ph0Ny5c7V8+XK1aNFCt9xyixo0aKAnnnhCK1eu9M0oHThwYKk6LLwz2vPz8yVJO3bs4HWomvLOVO/WrZsmTpyopk2b6rLLLpPH49HChQuLdVo4nU5FRUX5btekSRPdfffdio2N1bx585SVlSVJql+/vrp27aphw4apUaNGFVY/hI63k3PVqlXKzs7WlVde6euo8K6GYLVa9fe//102m01vvfWWTCaTZsyYIYvFoldeeUUWi8X3PuYVFxcnk8mkn3/+WXa7PeI6KiTpwIEDmjhxoiTpkUce8XU0eGfX9unTR/Xq1VPLli1Vv3593+0Kzyq9//77tWjRImVkZGjEiBF+nRVFV5xA1UTmQHkgd6CsyB0oL+QOlBa5I3jkDpQGuQPlgdyBsiJ3oDyQOVAW5I7gkDkAhBKDM1GMw+FQjx49/Gb1GYbhmw3x8ccfa+HChZKk7777TocPH1Z+fr42btyomTNnavXq1XrhhRcUHR0tqWDWRFpamrZv366UlBRNnDhRLVu2DH/FEHZbtmzR2LFjJUlPPPGEr6PCO7vmjDPO0EUXXaSlS5fKZrPJZrP53d77oaR58+a655579OGHH2rRokX6888/JRUs2X/++edr2LBhdFREkJycHP3666/Kz8/XX/7yF3Xv3l1Wq1UvvfSSr8PixRdflKRSdViYzWaZzWb16NFDmzZtUlxcnCZPnszrUDVWq1YtSdKPP/6oiy++WHfddZck+XVavPrqq74VEzZv3qxatWopJSVFvXv3Vq9evTRlyhTl5eUpJydH5557rpo0aaLk5OQKqxNCy2QyyTAMzZ8/X4Zh+N5jCl++o/As41mzZmnDhg2yWq2aNm2aGjZsqIceeqhYuLbZbDIMw9dREYmdpkeOHNH69etVp04dXXzxxZIK3ve9HRKHDx9WQkKCJOmjjz7Stm3bFB8frxYtWuiSSy5RQkKCzGazJk+erFmzZunCCy+URGdFJCJzIJTIHQgGuQPljdyBUyF3BI/cgdIidyCUyB0IBrkD5YnMgdIgdwSHzAEglBicCT/R0dG64YYb1LNnT3Xt2tW33dtZ8f777+vtt9+WJD388MPq3Lmz2rVrp5UrV+qjjz7SqlWr9MMPP6hRo0Z66KGH5PF4FB0drVtvvVV2u10vvPACAaEaWbp0qTZt2qQLL7xQt912m6SCDxze9mQ2m3X++efrjTfe0L59+7RmzZqA7SM6OlrnnHOOevXqpS+++EJ5eXmy2Ww699xz1bRpU6WkpISzWihnKSkp6tu3rxo0aKAHH3zQt1x+o0aN9PLLL+vxxx8vc4eFJPXp00fZ2dm65557eB2q5jp16qQmTZooMzNTDodDHTt21D333CPpRKfFo48+qnfffVeTJ0/Wv//9bw0ZMkQ33nijrFarzGazrr322gquBcpb4dmNe/fulXTi85B0okM9MTFRsbGxSkxM1HnnnacffvhB06dPV8+ePdW7d2+/c3bp0kWNGjXS3r17NWnSpIh8LVqzZo0OHTqk1NRU36xb6cRjN3XqVG3ZskU7d+7UkiVL5HQ6JRW813/77bd69913lZSUJLPZTGdFBCNzINTIHQgGuQPljdyB0iB3BIfcgdIgdyDUyB0IBrkD5YnMgdIid5QdmQNAKDE4E8Wkpqaqe/fukk4scy1J06ZN02uvvaaoqCi98soruuiii3y36devn1JSUjRmzBj9/vvvWr16tUwmk+/NqX///urXr5/i4uLCXyFUiLy8PH3wwQfKz8/Xeeed5/ugUfgDh8vlUkJCgtLS0rRv3z7l5uaWeD7DMBQVFeVb7huRyfuh9IYbbpDb7VZUVJTfzK2GDRuWqsOi8GuXV//+/dWnTx9eh6o5wzBktVpVq1YtrVu3Tjt27FDLli3Vrl07DRkyRIZhaNGiRZozZ44uv/xybdq0SVJBJ5p3lQREPo/HI5fLpcTEREkFlwfKyclRYmKi3+uL2+2Ww+GQ2+1W/fr1dfHFF2vdunXatm2bVq1aVayzokWLFho1apSaNWumhg0bhr1e4VCnTh3FxsbK4/Ho559/Vq1atVSjRg1J0sSJE/Wvf/1LknTppZeqVq1aSktL03fffaddu3Zp+fLluuuuu/TBBx/4HntJdFZEKDIHQoXcgWCQO1DeyB0oDXJH8MgdKC1yB0KF3IFgkDtQnsgcKC1yR3DIHABCib9+nFThD/sXXnihevfurZdeesnXWWEYhjwej6SC2Tk33HCDJGnRokXavn2777ZRUVEEhGomPj5eV199tZo3b67zzz8/4DFWq1UWi8W3fLq3vRmGUexY75LriDzbtm1TTk6OpIIPpYZhyGw2KyoqSpJ8HRVe3g6Lrl276tChQ3rxxRf1448/+i4T4w0SNptNK1eu9Lt8DK9D8LaPPn36yGaz6Y8//vDta9++ve69917169dPUVFR2rJliyRp2LBhGjx4sO/9DpHPbDYrOjpagwYNkiQtWbJEr7/+uqQT71Uul0sWi0ULFy7UmjVr1LNnT/Xv31833nijpIJwvnv3bt85DcNQTEyM+vbtG5EdFV5nnHGG0tPTdeTIEX366acaM2aMXnvtNT322GN69tlnJUljxozRM888o0ceeUS33XabXn75ZV1++eVKSkrS6tWr9cUXX1RwLRBuZA6cDnIHSovcgXAid6A0yB3BI3cgGOQOnA5yB0qL3IFwIXOgtMgdwSFzAAglBmeiVFwul6Kjo/Xhhx/qkksukXTiQ5/ZbPZ9iKtXr55MJpMSExMVExNTkUVGJXDfffdp3LhxSk5ODvhB39v54J117F3u28vtdvv9XnRmIKq+zz77TBdffLGmT5/uWxK+NM9zoA6LWbNmKTc3VyaTSTk5Obr77rt1xx13aMmSJeVdDVQShTvRvb8X5Z2V1qJFC0nS5s2bJUkOh0OS1K5dO9WvX19Op9N3royMDNntdpnNZr9LFyDy9erVS9dcc40kafLkyRo5cqRWrlwpu92urKwszZgxQ/fff79MJpPvEmnXXHONWrduLZfL5fc+Vh3ewzwej9LT0/X666+rZs2ayszM1PTp0/X+++9r2rRpkqRHHnlE119/va8j2jAMtWjRQtddd51q1qwpj8ej1atXV2Q1UIHIHAgWuQOnQu5AKJE7EGrkjrIhd+B0kTsQLHIHToXcgVAhc6A8kDtKj8wBINS4rDlKxfumUnimX+E3Xe+Hwq1bt8owDHXq1En16tULf0FR6XhnywRaptvlcikqKsrXSeH912Qyye12+zoxvv32W7Vq1Upt27YNU6kRDl999ZXGjBkjSYqJiSk2Y/RUil7y44UXXlBUVJS6d++uUaNG6ddff5XValWrVq3KofSojEwmk997U0nvWZLUunVrmUwmLVmyRHfddZfvMh6ffPKJPvzwQ0lSmzZtlJmZqR9++EFut1svv/wys5GrmZSUFF1xxRXKycnR999/rylTpmjevHmqUaOGHA6H9uzZI0l6+OGHfV/oxMbGKjExUUePHtWBAwd8qyVEkkB/U97LNHk8HnXs2FFffvml3n//feXk5Mhqtaply5b67LPP1K1bN79zeM/TsmVLXXTRRRo7dqz27dvnu6wKqhcyB04HuQMlIXcg1MgdCDVyR2DkDpQXcgdOB7kDJSF3IJTIHCgP5I7iyBwAwoXBmQhK4Tcpb6h0uVyaOXOmzGazzj33XEmB39AQuQI93yd7/r2dEfHx8ZJOdIw5HA5feBg+fLi++eYb3XrrrWrZsqVvO6q2r776SiNGjJAkPffccxo8eHBQ5/F2WDz22GNatWqVnn76adWvX1/r169XamqqJk6cGLHL6cPf6tWrtWHDBs2ZM0e1a9dWamqqLrroIjVo0EBJSUm+MOXVpEkTtW7dWrt37/Z1un/yySd67rnnJEnPPvusevfurRdffFFLlizRrFmzNHLkSL322msVUj+Uj6NHjyopKSngPu97Wvfu3RUTE6PGjRtr3LhxysrKUlZWliSpc+fOuuyyy3TTTTdJKuh0d7vdvksXWSyWiPwslJWVpaNHj2rVqlWyWq3q27evUlNTJRV8OeF2u1W/fn2NHDnS917/7LPPavfu3apbt66k4p8lJWnHjh2SpFq1atFZAUlkDpSM3IHSIncg1MgdCAa5IzjkDoQLuQMlIXegtMgdCCUyB4JF7ig7MgeAcGFwJk5L4dl+w4cP16pVq9S1a1ffbIpIenNGYPPmzVNeXp4uueQSmUymMn0o84YHbxvy/u7tkBg1apS++eYbxcXF6dprr6WjIkIU7qj45z//qauuukpSweuJ2WyWyWSSx+PxzTI6VZtq2LCh/v3vf+uBBx5QRkaGsrOzlZycrIkTJ/ou54DINmPGDD3//PM6dOiQ36U+fvjhB7Vs2VKPPvqomjVr5tvuvVxHgwYNtGHDBm3YsEFr1qzxdVY89dRTuvrqqyVJd911l44dO6Y//vhDd911VxhrhfL2zTffaMmSJfr73/+u1q1bF9tf+PWnQ4cO6tChg/r3768//vhDBw4cUJs2bdSsWTNfh6jdbldMTIz27dungwcPqn379mrfvn3EfRaaP3++Jk6cqLVr1+rw4cOSpKZNm2rIkCG64IILlJiYWOx9XZK2bdumxMREZWZmqmHDhn5feFmtVuXn52vHjh2Ki4tTv379JPHFF04gc0Aid6DsyB0INXIHgkHuCA65AxWB3AGJ3IGyI3cglMgcCBa5o+zIHADCicGZCIr3DcT7hvTYY49p2rRpql27tl544QXVqlWrgkuIcPjiiy80atQoderUSdHR0brgggvK1GHh/ZBis9kkSfn5+b59o0aN0hdffKHk5GRNnjyZ0BkhinZUXHPNNZIKZl9FRUX5jiv8Ifdkl2vwzhBMSkpSbGysJCkpKUmTJk2izVQT06dP19ChQyVJV155pZKTk2U2m/Xdd99p586dyszM1PLly/Xaa6+pT58+MpvNvlnrffr00U8//aS33npLv/zyi6SCzgrvrECPx6MOHTpo2LBhSk5O5hJWEeSbb77R8OHDZTKZFB8frxtvvDHgJYGKvuZ07NhRHTt2LHacw+FQTEyMJOmll15SVlaWBg0aJI/HE1Ghe+rUqRo2bJgkqW7duqpfv74cDoe2b9+ut956S40bN1bXrl39LuPhna3tdrt19OhRvffee+rWrZvvNdv79zhixAitWbNGnTp1Ut++fSXxxRfIHDiB3IGyIncg1MgdCAa5IzjkDoQbuQNe5A6UFbkDoUTmQLDIHWVH5gAQbgzORFBMJpP279+vzMxMvfXWW1q2bJkaNWqkcePGqUmTJhVdPITB3LlzNWrUKEnSb7/9pgkTJsgwDA0YMKDUHRbe/d5/U1JSJBUEhi+//JKOiggzefJkPfPMM5Kk119/3Tfr3HtZF6fTqdWrV+vXX3/Vli1bFBsbq9atW2vgwIGqXbu2zGZzscs1mM1m5eTk6LHHHtOSJUuUnJysSZMmqWXLlhVSR4TXmjVr9MILL0iSnn76aV133XW+fTfccIM+/fRTzZs3T3/88YcefvhhjRw5UhdffLEvVKanp8tsNpfYWeF9bWrTpk04q4VytmTJEt9rkWEYmjp1qtxut2655ZaAHRaBeN/jvP9GR0fLMAw9/vjjmj17tpo3b65bb701olZAmDFjhq+z4oEHHtBll10mq9WqzMxMjRo1Sjt37tT48ePVtWvXYrc1mUy66aabtHbtWi1dulS33367rrvuOtWrV09HjhzRlClTNG/ePKWnp+ull15SnTp1wl09VFJkDkjkDpQduQOhRu5AMMgdwSF3oCKQOyCRO1B25A6EEpkDwSJ3lB2ZA0BFYHAmgnL06FE98sgjWrlypaxWq84++2yNGTNGjRo1quiiIQw2b96sDz74QJLUrVs3rV27VsuWLfPtL22HhXc2svfDnGEYGjNmDB0VEWj37t2aMGGCpIIZSN26dZMk2Ww2xcXFKT8/X6NHj9bSpUu1b98+v9vOnTtXF154oa6++mpZrVa/duVwOHTPPfdo2bJlqlmzpj7++GPaTDWyZcsWZWdn68orr/R1Vng8Hnk8HjVq1Eh33323OnXqpPHjx2v16tX65z//KbPZrMsvv1ySdOGFF+qWW27RhAkTNHz4cL/OisKdYogcu3bt0uTJk2Wz2TRw4EC53W799NNPmjZtmiSVusOicGd7dna2Fi5cqC+//FJLlixR3bp19dZbb6l+/frlWpdwWrBggUaOHCmpYKWHG2+80bevfv36GjRokMaOHev7uwm0AkCHDh101VVX6auvvtKKFSu0efNm5eXlyWKxyG63q3Hjxho7dqyaNm0a3sqhUiNzgNyBsiJ3oDyQO1BW5I7gkDtQUcgdIHegrMgdCDUyB4JB7ig7MgeAisLgTAQlKSlJf/vb3xQTE6P+/fvr4osvVs2aNSu6WAgDm82mmTNnav369WrTpo1Gjx6t9evXa/To0WXusPCGAqfTKUl6++23lZWVRUdFBKpZs6YefvhhffTRR1q9erX++te/6pVXXlH79u2Vk5OjO++8U6tWrVLNmjXVoUMH3wyjpUuXavHixTpw4IDcbrduuOEGvyBptVpVt25dmc1mOiqqEe/ryty5c+VyuZSQkCCp4FICFovF10aSk5P1l7/8RTVr1tRrr72mVatW6emnn1atWrXUu3dvSdLw4cM1cOBAXwcanRWRy+PxaMGCBfrll1/UoEEDXXvttTr77LP15JNP6ptvvvF1WNx8880644wzSnVOwzC0Y8cOjR8/XuvXr1fv3r31zDPPRNQXOH/88YfGjh2rvLw8PfLII77OCu/fm3TiMl1RUVHyeDzav3+/kpOTFR8f7ztP3bp1dd111yk9PV0fffSRsrOz5fF41Lx5c3Xo0EH33HOPGjZsGP4KolIjc1Rv5A4Eg9yBUCJ3IBjkjuCQO1CRyB3VG7kDwSB3IFTIHAgWuaPsyBwAKhKDMxG0AQMGqHfv3oqNjZXVSlOqLo4ePapp06b5ZuG0atXK96GurB0WhmFIKvjQI0lZWVlKSkrSpEmTCJ0RJjY2Vueff74sFovGjRundevW6YknntBLL72k//73v1q1apXatWunl19+WXXr1lVCQoIMw9D06dM1YsQIbdmyRT/88IPOOusstWjRwtemzGazXnzxRQ0fPlxpaWkVXU2ESdFLA3lDk/ffwqKjo9WlSxc98sgjevXVV7V69WoNHTpUn376qe/SVHRWVA8ej0eZmZmy2Wzq16+fevToIZPJpOeff953uY+ydliYTCa1bNlSjz76qPbv36++ffsqPT29vKsSNk6nU0uXLtWqVat02WWX6e6775bkfykcu92uPXv2SJLi4+N13333ac2aNUpOTtaAAQM0cOBAtW3bVpLUvHlzNW/eXIMGDdK2bduUm5urbt26KSYmxtfxCBRF5qi+yB0IBrkDoUTuQDDIHWVH7kBlQO6ovsgdCAa5A6FC5kCwyB1lQ+YAUNFImTgtiYmJFV0EhFlaWpquuuoqZWRk6M477/R9YBk8eLCksnVYeMNF9+7d9f333ys2NlaffvopHRURKiYmRv369ZMkX4fFkCFDlJWVpbZt2+q///2vatSoIZfL5bvNoEGDZDab9Y9//EPLli3TrFmz1KJFC7+2ZLFY6Kiopryz9f73v//psssuU/v27QMeZ7FY1LlzZ9166616++23tX37do0dO1ZPPfWUX0iisyKyWa1WXXvttUpNTdVf//pXRUdHy+FwKDo6Wi+++KIkBdVhER8frz59+pzy0lZVUVRUlKKiotS1a1ddeeWVvu2GYfjew8ePH6+ZM2dKklauXKnc3FxFRUVp69at2rNnj1asWKEnnnjC9/fp8XhUr1491atXL/wVQpVF5qieyB0IFrkDoUbuQFmQO8qO3IHKgtxRPZE7ECxyB0KJzIGyIneUDZkDQEVjcCaAMomKitLf/vY3389ut9s3o6+0HRZFP9Cdf/75OnbsmC644AI6KiKct8PCZDLpP//5j9atW6cGDRronXfeUY0aNeR2u4vNTr/kkkv0/fffa9asWVq1apU8Ho8kwmV15n0N6datmxo0aKBDhw5pzpw5aty4sZKSkgLeJjo6WmeffbaWL1+u7du3a926dTp8+DAz2CKct614Zwo3a9ZMd9xxh8xms1wul6Kjo32XrDhVh4V39YPC71+FZyBHUkdFYVdffbXOPPNMnXnmmb5t3s6KcePG6d///rekgsvm9OrVSw0bNlReXp5eeOEFzZo1S2vWrNGUKVPUvHlzxcbG8toNoNTIHTgd5A6EArkDpUXuOH3kDgAVhdyB00HuwOkic6AsyB2nh8wBoCLxigGgzLyzS6SCDy1ms9kXIAcPHqynn35aMTExWrZsmT7++GPNmjVLUsEHOZfLJZPJpPz8fG3YsEGSVK9ePd11111q2bJlxVQIYRUTE6Nzzz1Xd911l7p06aIrr7xS9erVk8fj8btUg7djS5JSU1MlSS6XS2azOSJDAQLztgFJvtcZ77aOHTuqXbt2stlsmjJlilasWHHSc6Wmpuq2225TcnKyNm/erNmzZ5dfwVEpeF8rvCG5cOeCt2PUYrH4Ljf14osvavDgwbLZbJo2bZomTpyoTZs2+Z0vNzdXn3/+ufbv3x/x4dv7N9euXTuZzWa/v8eVK1fqX//6lyTpzTff1G233aY2bdooMTFRtWvX1nPPPaezzjpL+fn5+vnnn5Wfnx/xjxeA0CN34HSQO1AW5A6cDnLH6SF3AKho5A6cDnIHSovMgdNF7ggemQNARWPlTAAh4e2wKGlGqWEYuuCCC2S1WnXkyBENGzZMW7du1fPPP6/u3bvzIaaa8c4oTUpK8i33HqgNeGd4ZWVlSZIaNGggKTJnbCEwk8kku90uu90uh8Oh9PR0v7A5ZswYbdu2TZs3b9Zzzz2ntLQ0derUKeC53G63mjVrpnPPPVfTpk2T3W4PZ1UQRpmZmdqxY4cWL16smjVr6swzz1SPHj0UFRXl12Hh5e2wKGlG6Y033qg2bdooNzdXDz74oBYuXKg777xTDz/8sF8na1W3c+dO5efnq1WrVpKKvy4Xfu3t2rWrHnzwQTVt2lQXXnihpBOdQW63W/Hx8br//vv122+/ac+ePcrMzOSSTABCgtyBsiB3oLTIHQgGuSM45A4AVQG5A2VB7kBpkDkQLHJH2ZE5AFQ2DM4EEDKn6rCIiopSjx49NGzYMM2dO1fp6em+8InqJzY2Vr169Sqxo8p7yY+jR49qx44dSklJUf/+/SWp2KViEJmWL1+uhQsXavbs2crNzZXVatXFF1+sq6++Wg0bNpTJZFJaWpoeffRRPffcc9qxY4ceffRRvfLKK+rcuXOx83nbjNPplCTl5uaGszoIk9mzZ+udd97R1q1b5XA4JEkNGzbUwIED9cgjj/hWQijqZB0WFotFV1xxhd577z0tXLhQ8fHxuuqqqyKmo0KSfvnlF40bN06rVq3SG2+8oQEDBpzyNvfee6/vZ8MwfK/n3sfF4/EoLy9P0onVAXjtBhAK5A6UBbkDp0LuQDDIHcEhdwCoSsgdKAtyB06GzIFgkTvKjswBoDJicCaAkDpZh4Xb7dY777yjtWvXKi0tTRMmTPDNIkT1dLKOCu8H3jFjxmjLli06++yz1aFDB0nMJK0Opk+frmeeeUZHjhzx2z5u3Dht375dd999t9q2bStJ6t69u+6++26NHTtWO3fu1NChQ/XEE0+oe/fuvtlr3jZlGIb27t2rxMRE9ezZM+z1QvmaOnWqhg0bJklq27at0tLStHr1au3bt0/Tpk1T586dNXDgwBKDc9EOC8Mw9O2332rq1Kn65ZdftGvXLqWmpmrixIlq2rRpmGtXfr799luNGDFCLpdLffr0UXZ2dpnPUfjxdLlcslqt2rVrl9xut7p3766OHTvy2g0gpMgdKAtyB0pC7kAwyB3BIXcAqIrIHSgLcgcCIXMgWOSOsiNzAKisGJwJIOSKdlhERUXpySef1G+//Sa3262UlBRNnDhRzZs3r+iiopLxBghvR8Xjjz+uGTNmqF69ehozZgzLxFcT3377rZ544glJ0lVXXaXWrVvLarVq48aN+vzzzzVz5kylpaVp9OjRkqSEhAQNGDBAZrNZ//nPf5SZmal//vOfuvTSSzVgwAB169ZNJpNJHo9Hw4cP12+//aauXbvqzDPPrMhqIsRmzpzp66gYPny4rr/+esXExCgjI0MvvviiMjIytG7dOg0cOPCkwdlisfgC90svvSSXy6Xvv/9eu3btUnJysiZOnKgWLVqEq1rl7ocffvD9vf3jH//QnXfeeVodC95VACTpq6++kiT17t1bHo9HJpOJTgsAIUXuQLDIHZDIHQgOuSM45A4AVRm5A8Eid4DMgWCRO8qOzAGgMmNwJoByYTabfR/2Lr30Uo0fP17r169XcnKyJk2aREcFAjKZTNq/f7+2b9+ud999V0uXLlX9+vX1wQcfqFGjRhVdPITBggULNGLECEnSqFGjdOONN/r2ORwOpaen65133tGnn36qgQMHqnfv3pKkpKQkXXTRRapTp45eeOEFbd68WRMnTtRnn32m/v37Kzc3VwcPHtSaNWtUu3ZtPf/886pZs2aF1BGht2HDBr322muSpBEjRuiWW27x7evcubNuuukmZWRkyGazFbtt4ZnrXoV/z83NlcfjUVJSkiZNmhQxHRWStHbtWr300kuSClZ9uOGGGyTppJ0L3i8jAvF4PL7Hbvjw4Vq6dKnat2+vq6++2teJAQChRu5AMMgdIHcgGOSO4JA7AEQCcgeCQe6o3sgcCBa5o+zIHAAqu8CvNgBwmgzDkNVq1ZEjRzRkyBCtX79eKSkpmjx5csR80EPo5eTk6PHHH9dtt92mpUuXqmvXrpowYQKdW9XEzp079dFHH8nlcunhhx/2dVa4XC5JUnR0tAYOHKgmTZrIbDbrwIEDkgpebyQpLi5Offr00eTJk3XllVeqRYsWys/P1/Tp0zVv3jzt2LFD3bt318cffxwxl2io7rzP/caNG7Vnzx717t3b11Hh8Xh8bUeS6tSpo/T0dO3cuVNz5szR6tWr5XA4inVUSAUdpzk5ORoyZIjmzZvne/9q2bJleCpWzryP25o1a7R3717dcsstvs4Kh8Mhs9kswzCUl5en9evXa9OmTfrzzz8lybcv0Pm8X1QMHTpU33zzjdLT0/Xqq6+qTp06YawdgOqG3IFgkDuqN3IHyorcERxyB4BIQu5AMMgd1ReZA8Egd5QdmQNAVcGwbgDlwmQyyWaz6R//+IcWLlyo1NRUZpDilBITE3XLLbfIbrerd+/euuGGG1S7du2KLhbCZO3atVq9erXvuZcKAmfhWWitW7dWw4YNlZmZqTVr1ujyyy/3O4fb7VaNGjX07LPPaseOHVqxYoWOHj2q3NxcnXXWWWrRogWXi4lAP//8s1wul+/1wu12S5Kv7axfv1779+/XV199pSlTpmjHjh2Kj49Xq1atNGbMGLVu3dpvhqTL5dLw4cM1b948paamRtSlPaSC92in06np06dLks455xxJBZ0V0dHRstvteuONN7R69WplZGTIZDKpRYsW6tq1qx577DHFx8cXO9+uXbu0bt06TZw4UcuWLVPTpk31zjvv0DkIoNyROxAMckf1Ru5AsMgdZUPuABBJyB0IBrmj+iJz4HSQO0qPzAGgqmBwJoByc/ToUa1YsUIJCQl0VKDULrjgAnXt2lXx8fGKjY2t6OIgTNxutz788EMdO3ZMffr0UVJSkiT5BUin06moqChfIPVesqHw5QgsFosMw5DFYlGzZs3UrFmzMNYC4eZ97r0Bes2aNdq3b5/q1q3rO2bixIn66KOPJBW0j+TkZNlsNjkcDv3222969NFH9dxzz6lz586+S35YrVZ17NhRq1ev1ocffhgxHRWFOZ1OHT16VDVq1FDjxo0lFczYttlsuuuuu7Rs2TJZrVbftq1bt2rjxo3atWuXhg4dqjZt2vjOlZOTo3HjxunLL79UTEyMzjvvPI0YMYLLMwEIG3IHgkHuqJ7IHQgGuSN45A4AkYTcgWCQO6ofMgeCRe4IDpkDQFXA4EwA5aZOnTqaNm2aTCaTGjZsWNHFQRXCbL/qx2KxaPTo0Ro9erQuvPBCmUwmGYbh1xkRFRUlSb7XE4/H4/u3cMeG9zZFtyPyeNvIGWecobi4OO3bt0/PPfec7rjjDuXl5Wnp0qX6z3/+I0kaMWKEBgwYoLp162rHjh1aunSp3nnnHW3dulVPP/20Pv/8c0VHR/vazZ133qnrr79eNWrUqOBalg+TyeSbaet0OiVJdrtdDz/8sJYtW6a2bdvqwQcfVHp6un7//XetXLlS//vf//TLL7/IMAy98cYbSkxMlGEYiouL0//93/8pJiZGrVu3Vv/+/XkdBxBW5A4Ei/er6ofcgWCQO4JH7gAQScgdCBbvV9ULmQPBIncEh8wBoCpgcCaAcsVMEgCl1b59e02YMEHR0dGS/GeJFubd7+2w8PLOApQKQqzZbC7W6YHI4n1uBwwYoOnTpysjI0OzZs3SggUL5HK5fEH8qaee0k033STDMCRJjRs3Vnp6ugzD0CuvvKLff/9d33//va644gqZTCZfh0UkdlR4xcTEKDU1VevXr9c333yjoUOHasmSJZo/f77atm2rjz/+WImJiZIK/jbPPvtsNW3aVG+//bYWLFigV155RU8//bRMJpMsFou6dOmi9u3by2w2+/4OASCcyB0ASovcgbIidwSP3AEg0pA7AJQGmQPBIHcEh8wBoCpgigUAAKg0EhMTfR0SRbndbknyBU7vv2az2a+zYty4cZoyZYo8Hg+dFdVE3bp19corr2jw4MFq3ry5zGazGjZsqI4dO6pz584aNGiQJP9OsPj4ePXq1ct3OaHs7GzfMZE+C9nboXfRRRfJarVqzZo12rFjhzIyMiRJY8aMUWJiolwul+/vrH79+rriiit01VVXyWq1auXKlTpw4IDfeaOiouisAAAAVQK5A8Egd5QNuQMAAFRnZA4Ei9xRemQOAFUFK2cCAIAqwRs0vZcn8HI6nb7LgIwcOVJTpkzR2WefrYsvvtg3Gw6Rr1GjRhozZozy8vKUm5ur9PR0/eMf/5DD4VBycnLA29SqVUvx8fGSTlzuojrw/i116NBBMTExWr58ud577z1ZrVbFxcX5/m6K/q3VrVtXPXr00GeffabNmzcrKytLtWrVCnv5AQAAyhO5AydD7ig9cgcAAEBgZA6cCrmjdMgcAKqKyB0mDwAAIop3dl/hWX4ej8fXWfHUU09pypQpSkpK0vDhw+msqIZiY2OVlpamRo0aKTMzU3PnztXhw4eVk5Pjd5zL5ZIk7d+/X8eOHVO9evXUu3dvSSdmKUeSkurUpk0bPfnkk5KkqVOnasaMGXI4HL6/scKX0/Geo3v37kpPT5d04nEEAACIJOQOnAq5IzByBwAAQOmQOVAa5I7iyBwAqioGZwIAgCrBG568IcntdvuC1VNPPaUvv/xSycnJ+vTTT9WyZcsKKycqh4SEBNWqVUsHDx7U8uXL5XA4JBW0I+8syX/96186dOiQzjzzTDVq1EiSIubyMPPmzdOMGTMkFdSpaKeF9/eLLrpIf/vb3yRJeXl5crvd+vDDD2W322U2m31/d95L7Rw8eFC5ublq1aqVmjVrFq7qAAAAhA25A2VB7iB3AAAAlBWZA2VVnXMHmQNAJGBwJgAAqBK8AcsboFJSUiQVXN7D21kxefJktWjRoqKKiEokISFB8fHx2r9/v8aOHavZs2fr6NGjMgxDBw4c0EMPPaQff/xR9evX1/Dhw5WUlFTRRQ6ZL774QkOGDNGECRM0e/ZsScU7LbydMgkJCbrmmmt07bXX+vYtXbpUn3/+ua/TwjAMXwfP+++/L5vNpo4dOyo6OjqMtQIAAAgPcgfKgtxB7gAAACgrMgfKqrrmDjIHgEhhMiJtLWMAABDRPvroI7300ku69tpr5XK59PXXX9NZgYAWLFigRx55RMeOHVN6eroSEhKUkJCg7Oxs7dmzR3Xq1NFHH32k5s2bV3RRQ2bu3Lm65557fL/36NFDt956qwYMGCCpoOMv0GzZP/74Q1988YX++9//SpKaN2+uc845R7fffrvMZrPcbrdeeeUVTZ8+XfXr19eECRN8s28BAAAiEbkDpUXuIHcAAAAEg8yBsqhuuYPMASCSMDgTAABUKePHj9crr7yiuLg42Ww2OitQIrfbrRkzZmj06NHKy8vzbU9LS1ObNm00ZswYNW7cuAJLGFqbN2/WmDFjtGLFCnXr1k1r166V3W4vdadFdna2ZsyYoWeeeca3LTU1VVFRUfJ4PMrKylLDhg01btw4/t4AAEDEI3egtMgd5A4AAIBgkDlQFtUpd5A5AEQaa0UXAAAAoDS8Ict7qQ86K3AqFotFl112mdq2baupU6fq0KFDiouLU9++fdWhQwelpaVVdBFDxmazaebMmVq/fr3atGmj0aNHa/369Ro9erSWLVvmO27AgAG+y34U7bRISUnRjTfeqDPOOEPff/+9lixZoszMTLlcLp155pnq16+fhgwZwixSAAAQ0cgdKCtyB7kDAACgLMgcCEZ1yR1kDgCRiMGZAACgSvCGq549e0qSzGYznRUolRYtWmjo0KEVXYxydfToUU2bNk02m00DBw5Uq1atdMYZZ0hSqTstvL93795dHTt2lNvt1q5du2Sz2dS6dWsZhqHY2Niw1w0AACCcyB0IFrmD3AEAAFAaZA6cjkjPHWQOAJGIy5oDAIAqZ/Hixapbt66aNWtW0UVBFVE4nJd0qYuqzOl06qOPPlJGRobefPNNRUVF+fZNnTpVo0ePLtNlP9xutywWi98xkfi4AQAAnAy5A2VF7iB3AAAAlAWZA8GI5NxB5gAQiRicCQAAAEQAp9MpSYqKipLb7ZbJZJLZbJZUuk6LkmaXAgAAAIAXuQMAAABAeSJzAIg0XNYcAAAAiACFZ5B6Z4J6PB6ZzWYNHjxYUsmX/XC5XLJarcrPz9f27dvVpk0bOisAAAAAFEPuAAAAAFCeyBwAIg2DMwEAAIAIZTabT9ppYRiGLrjgAlmtVh05ckTDhg3T1q1b9fzzz6t79+4VWHIAAAAAVQW5AwAAAEB5InMAqMoYnAkAAABEsFN1WkRFRalHjx4aNmyY5s6dq/T0dDVo0KACSwwAAACgqiF3AAAAAChPZA4AVZXJMAyjogsBAAAAoHx5Oy0kaerUqRo9erTsdru6du0qh8OhtWvXKi0tTZ988olatGhRwaUFAAAAUBWROwAAAACUJzIHgKqGwZkAAABANVG402L69Ol68skn5XK55Ha7lZKSosmTJ6t58+YVXEoAAAAAVRm5AwAAAEB5InMAqErMFV0AAAAAAOFhNpvlcrkkSZdeeqlatGght9ut5ORkTZo0ic4KAAAAAKeN3AEAAACgPJE5AFQlDM4EAAAAqgnDMGS1WnXkyBENGTJE69ev980i5fIeAAAAAEKB3AEAAACgPJE5AFQlXNYcAAAAqEZsNpvuv/9+LVy4UKmpqcwiBQAAABBy5A4AAAAA5YnMAaCqYOVMAAAAoBo5evSoVqxYoYSEBDorAAAAAJQLcgcAAACA8kTmAFBVsHImAAAAUM3s3LlTJpNJDRs2rOiiAAAAAIhQ5A4AAAAA5YnMAaAqYHAmAAAAAAAAAAAAAAAAAABACHFZcwAAAAAAAAAAAAAAAAAAgBBicCYAAAAAAAAAAAAAAAAAAEAIMTgTAAAAAAAAAAAAAAAAAAAghBicCQAAAAAAAAAAAAAAAAAAEEIMzgQAAAAAAAAAAAAAAAAAAAghBmcCAAAAAAAAAAAAAAAAAACEEIMzAQAAAAAAAAAAAAAAAAAAQojBmQAAAAAAAAAAAAAAAAAAACHE4EwAAAAAAAAAAAAAAAAAAIAQYnAmAAAAAAAAAAAAAAAAAABACDE4EwAAAAAAAAAAAAAAAAAAIIQYnAkAABCkW265Ra1bt/b77+uvv67oYgEAAACIEGQOAAAAAOWN3AEAQPmxVnQBAKAqyczM1NSpU7V8+XJt27ZNR48eldvtVkxMjNLS0lSnTh21bNlSbdq08f0XFxdX0cVGNfD1119r+PDhxbb/9NNPatiwYQWUCAAAAMEgc6CyInMAAABEDnIHKityBwAAiDQMzgSAUnC73Xr11Vc1YcIEud3uYvtdLpdyc3O1c+dOLV++3Ld96NChuuuuu8JZVABBWLp0qW699Va/bQ0aNNCcOXMqqEQAAKC6IXMAkY3MAQAAKgNyBxDZyB0AAFQ+DM4EgFIYOXIky/cDAAAAKDdkDgAAAADljdwBAAAAhBeDMwHgFBYvXhywsyI9PV1dunRRWlqaXC6XDh06pC1btmjXrl0yDKMCSgoAAACgKiJzAAAAAChv5A4AAAAg/BicCQCn8N133xXbNmjQIL300kuyWou/jB44cEDz58/Xt99+K5PJFI4iAgAAAKjCyBwAAAAAyhu5AwAAAAg/BmcCwCls3Lix2Lbbb789YGeFJNWqVUtXX321rr76ajmdzlOe/+DBg/rqq6+0bNkybd68WdnZ2fJ4PEpLS1ObNm3Ur18/XXnllYqNjT3luXbu3Knx48drwYIF2r9/v+Lj49WsWTNdcskluv766xUdHa3WrVsXu91PP/2khg0b+n7ftWuX+vfvX+y4QI/F0qVLdeutt/pta9CggebMmVNiOd1ut2bNmqV58+Zp9erVysrKUl5enhITE9WoUSP16tVL1157rRo3blziOb7++msNHz7cb1vPnj31ySef6PDhw/r44481Z84c7dy5Ux6PRw0aNND555+v22+/XSkpKSWe1+vAgQOaOnWqVqxYoU2bNik7O1sOh0NpaWlKT09Xx44d1adPH5133nmKjo4OeI6cnBxNnTpVixcv1u+//67Dhw/L6XQqJSVFrVq10jnnnKNrr71WNWrUOGV5KoJhGFqwYIFmzZqljIwM7d+/X7m5uUpISFDdunXVo0cPXXXVVTrzzDNLPMfJ2ofNZtOkSZP0448/atu2bcrPz1fdunXVt29f3XnnnapXr95Jy5ebm6uPP/5YM2fOVGZmpkwmkxo0aKDzzjtPf/3rX1WzZk3dcsst+vXXX/1u98ILL+j//u//JCngfq/du3eX6u+lJOvWrdOkSZO0ZMkSHThwQPHx8WrdurWuueYaDRo0iA5NAADgQ+Y4gcxB5iBzkDkAAED5IHecQO4gd5A7yB0AAIQLgzMB4BRyc3OLbVu3bp3atm17yttGRUWVuM/tduvdd9/Ve++9J4fDUWz/3r17tXfvXs2dO1fvvPOOXnrpJZ199tklnu+7777TyJEjZbfbfdscDodWrVqlVatW6YsvvtD7779/yjKXt2XLlmnYsGHatWtXsX2HDx/W4cOHtXr1an344Ye67bbbNHTo0BI7hwL5+eef9dhjjyk7O9tv+5YtW7RlyxZNnz5dH3/8cYmB0+Fw6F//+pc++eSTgB1O+/fv1/79+7Vu3Tp9+umnmjp1asDAPnnyZL366qsB28+BAwd04MABLVq0SGPHjtXIkSM1ePDgUtcxHDZu3KgnnnhCv//+e7F92dnZys7O1oYNG/TJJ5/oiiuu0JgxYxQfH1/q869du1YPPPCA9uzZ47d9x44dmjRpkqZPn67x48erffv2AW+/ZcsW3XHHHdq7d6/f9k2bNmnTpk364osv9K9//avU5Qm1N998U+PGjZPb7fZtczgcWrp0qZYuXaqFCxfqxRdfrLDyAQCAyoXMEVpkDjKHROYgcwAAgKLIHaFF7iB3SOQOcgcAAKdmrugCAEBlV7NmzWLbxowZo0cffVQzZszQzp07y3xOt9utBx54QG+//XbAzoqiDhw4oDvuuEMzZswIuH/+/Pl64okn/Doritq8ebNuu+22Mpc1lGbMmKFbb701YGdFUW63Wx9++KEeeOABeTyeUp1/06ZNuvfee4t1VhS2e/duPfnkkwH32Ww23Xbbbfrwww9LNRO4JM8884yefvrpgJ0VRR07dkxPPPGExo8fH/T9hdry5ct17bXXBuysCOTbb7/Vrbfeqvz8/FIdf/jwYd16663FOisKy87O1tChQ+VyuYrtO3DggG699dZinRVFb3/vvfdq+/btpSpTKI0bN07vvvuuX2dFUd98842mTp0avkIBAIBKjcwROmSO4sgcgZE5AABAdUPuCB1yR3HkjsDIHQAAgJUzAeAU+vTpoxUrVvhtc7lcmjZtmqZNmyZJSk5OVrt27dStWzf169dPHTp0OOk533rrLf3000/Ftrdq1Upt2rSRJGVkZPh1hng8Hg0fPlxt27ZV06ZNfdttNptGjBgRMNR36tRJZ5xxhrKysrRw4UJlZmaWut6htmXLFj3++OPFypmUlKSePXsqJSVFO3bsKHbZhTlz5mjcuHG65557Tnkf3o6KhIQEnXvuuYqJidGcOXN09OhRv+OWLl2qDRs2+B5rrzFjxmjlypXFzmuxWNSlSxc1bdpUHo9H27dv15o1awJ2anz55ZeaNGlSse0NGzZUx44dFR0drXXr1mnz5s1++1999VV17NhRPXr0OGU9y9OhQ4d07733Fut8iI2N1VlnnaX09HTt27dPS5Ys8etMWLNmjZ599lk9++yzp7yPvLw8SVJ0dLT69u2rlJQU/fzzzzpw4IDfcdu3b9f8+fOLXXbmmWee0cGDB4udt0GDBurVq5fsdrsWLFigI0eOyGaznbQsF1xwgVq1aqX9+/dr9uzZfvsSEhICzvJNTEw86Tm9nSTNmzdX165dtW/fPi1YsKDYcRMmTKh0s4gBAEDFIHOEBpmDzOFF5ihA5gAAAIWRO0KD3EHu8CJ3FCB3AABwcgzOBIBTuOmmm/TZZ58VC1OFHTlyRIsWLdKiRYv01ltvqVWrVnr44Yd1wQUXFDs2KytLH330kd82i8Wi5557TldeeaVvm8vl0vPPP+8XfvPz8/X222/r1Vdf9W377rvvApZt9OjRuvHGG32/b9q0SbfccstJZ1qWpzfeeKNYwO/fv79effVVv0tELF26VHfccYffLNv3339fN9xwg1JSUk55P02aNNEnn3yiOnXqSJL++OMPXX/99Tpy5IjfcUuXLvXrsNiwYYO+/fbbYuc744wz9Oabb6p58+Z+2w8dOqSJEycqOjrat83hcOjNN98sdo5HHnlEd911l8zmggWrDcPQ+++/r9dee813jMfj0euvv65PP/30lHUsT+PGjSv2WHXq1En/+c9/lJaW5tu2efNm3XzzzX7t6euvv9bf//73Yo9VIKmpqZo4caJatmwpqWCG6PXXX19spvHSpUv9OiwyMzM1a9asYue78MIL9eqrr/qej0OHDum2227Tpk2bTloO7wzrpUuXFuuwSElJ0ahRo05Zl0BuvvlmjRgxwvecv//++35/t5L0+++/6+jRo0pKSgrqPgAAQOQgc4QGmYPMURiZg8wBAAD8kTtCg9xB7iiM3EHuAADgVLisOQCcQmpqqj744AM1aNCg1LfZvHmz7rvvvoDh9aeffio2U++CCy7w66yQJKvVqkcffVQWi8Vv+6xZs/zC/Ny5c4vdR8+ePf06K6SC4D1kyJBS1yGUcnNzNW/ePL9tUVFReu655/w6KySpV69eGjBgwClvX5KhQ4f6Oiukghl9ffv2LXZc0WD8/fffyzAMv21xcXF6//33AwbwtLQ0Pfjgg2rRooVv26+//lqs86hdu3YaMmSIL7hKkslk0l133aVatWr5Hbty5cqTXr4iHAJdTubZZ5/166yQCmY+X3vttX7b3G63vv/++1Ldz1133eXrrJCkWrVqadCgQcWOK/o8zZ07t9jzFBUVpREjRvh1HqWlpemJJ54oVVlCLS0tTY899pjfc37rrbcW+1s2DEO7d+8Od/EAAEAlROY4fWQOMkdRZA4yBwAA8EfuOH3kDnJHUeQOcgcAAKfCypkAUApt2rTR9OnTNXHiRE2ZMsW3lP+pvPvuuzr77LPVvXt337aMjIxix82cOVOtW7cu1Tnz8/O1bt06denSRVLBjLSizjvvvIC3Pf/88/Xiiy+W6n5Cad26dcVmkjqdTp111lmlPsfKlStPeVmEhISEYp0dklS7du1i23Jycoqdv6gLLrhAdevWLXUZAz2369atK/VzK0krVqwIGNzDYffu3frzzz+Lbb/ssstKfY5Aj2MggZ7L0jxP69atK3ZMt27d/DqpvM455xwlJycXmx1b3i666CLFxsb6bYuJiVFSUpIOHz7st71o/QAAQPVF5jg9ZA4yR1FkjgJkDgAAUBi54/SQO8gdRZE7CpA7AAAoGYMzAaCU4uLidOedd+rOO+/U9u3btXz5cq1evVoZGRnatGlTsRluXlOmTPHrsMjKyjrtshQ+R9EAJEn16tULeLuStpe3UNT5ZJda8apfv77fDD6vmJiYYtuKPl+HDh0qdkyrVq3KUMLQP7fhdvDgwdM+R2mep/j4+GKzUyX5zQb1Kvo8BWrv9evXL/G+6tevH/YOi4YNGwbcHqgdejye8i4OAACoQsgcwSNzhPccwSJzhAaZAwAAnA5yR/DIHeE9R7DIHaFB7gAAIDQYnAkAQWjatKmaNm2qq6++WlJByPziiy/09ttvy+12+x1bdLanyWQ67fuvqBloHo+nWIdAoKBfVLjqnJqaGnB70UsslJeq/NyGSkU8T4E6AipSRbdDAAAQGcgcJ5A5TqjKz22okDkqvh0CAIDIQe44gdxxQlV+bkOF3FHx7RAAgEjB4EwACIH09HTde++92r9/vz777DO/ffn5+X6/16xZs9jtO3bsqA4dOpT6/po3b+77OTU1Vfv27fPbv3fv3oC3K2l7UYFmZEoFdYmPj/fbtnv37lOeL1CdExMTdcUVV5SqPJLUpEmTUh8bjJo1a2rLli1+2zZv3lzmcxTVokWLMl3SpCztINQCld9sNuv6668vdWdMSkpKiEvlL9As1KLtv7A9e/aUZ3EAAADChsxxcmQOMkeokDkAAEB1Ru44OXIHuSNUyB0AAFQfDM4EgFP46quv1KlTJ7Vs2fKUxwYKfEVnlnXq1Elff/2137b4+HiNGjWqVOVxuVyyWk+8fLdt27ZYYJs3b55uv/32YredO3duqe4jISEh4Pa9e/eqRYsWvt8Nw9C33357yvO1a9dOUVFRcjqdvm25ubm6/fbb1aBBg1Pevmidy0OXLl20dOlSv22zZ8/W/v37VadOnVKdo1OnTsW2uVwujRw5ssROoKLHlnc9T6ZBgwaqVauW3+U6PB6PLr30Ur/L1ZTEMIwSL3kTKm3bti3W5pYtW6a8vLxinWm//PJLqS/zEWimZ9GZ4QAAAOWFzHECmePkyBxkDgAAgGCRO04gd5wcuYPcAQAAQufUn54AoJr76aefNGjQIN1+++2aOnWqjh49GvC4rVu3asqUKcW2t2/f3u/3888/v9ilCZYsWaLXXnut2MxTryNHjuj777/X/fffr6effrrY+Yr69ddf9fnnn/tt27Jli8aOHRvw/EUlJycrOTm52PYJEyb4/f7hhx9q06ZNpzxfQkKC+vbt67fNMAzdf//92rlzZ8DbuN1urVy5Ui+//LLOO++8UpX7dFx88cXFZkzabDbdeeed2r59e7Hjjx07pv/85z/6448/fNt69uxZrNMqMzNTTz75pI4dOxbwfvPy8jR37lw98cQTuvfee0+/IqfpoosuKrbt8ccf14YNGwIebxiG1q9fr7ffflsDBgwo99mb559/frHnKScnR//85z/9OhgOHz6sV155pdTnDdRJl5WVpcOHDwdfWAAAgFIic5xA5jiBzFGAzAEAABAa5I4TyB0nkDsKkDsAAEB5YeVMACgFwzC0YMECLViwQCaTSc2bN1fz5s2VkpIih8OhnTt3KiMjQx6Pp9htL7/8cr/fa9eurdtuu03vvfee3/b33ntPn332mbp06aL09HRJUnZ2trZt26bMzExfGLvyyiv9bnfZZZfpzTff9JsBKEmjRo3S119/rTPOOENZWVlasGCBHA5Hqevcq1cv/fjjj37bPv/8c23evFktW7bU77//rjVr1pT6fI888oh++eUXvxml69ev14ABA9S5c2c1aNBACQkJys3N1a5du7R582bl5uaW+vynq02bNho8eLC++eYbv+0bN27UJZdcoq5du6pp06Zyu93atWuXfvvtN9ntdvXr1893bExMjB588EGNHj3a7xzffPONvv/+e3Xr1k116tSRxWLR0aNHtW3bNm3bts33mPTs2TPk9XrrrbdKnB3s1aZNG1177bWSpHvuuUdTp07162DZvXu3rrjiCrVt21ZNmzZVjRo1lJeXp71792rz5s2lnrEZCo0bN9bAgQM1c+ZMv+1ff/21li9frh49eshut5dpJqn3vGaz2e9v2OVy6aqrrlLPnj19M1ULP1YAAAChROYoQOYgc5A5yBwAAKD8kDsKkDvIHeQOcgcAAOHC4EwAKCPDMLR161Zt3br1lMfedNNN6tixY7HtDz30kDZu3Kj58+f7bT969GixbacSGxur5557TkOGDCnWYZKRkaGMjAzf761bt9bGjRtLdd5bb71Vs2bNKnbphpUrV2rlypW+39PS0nTo0KFTnu+MM87Q888/r8cff9zvnIZhaNWqVVq1alWpylWeRo8erczMTL/6SQUzW5ctW6Zly5ad8hzXX3+91q9fX2w2b35+vhYuXBjS8pbG1KlTT3lM//79fSG8Zs2aevvtt3XnnXcW6+Bav3691q9fXx7FLJOnnnpKy5cv18GDB/2279ixQzt27PD9npiYqKSkpFLNcE1ISFCvXr20ePFiv+27d+/268Qq/FgBAACUFzIHmeNkyBzlj8wBAACqA3IHueNkyB3lj9wBAED1wGXNAeAUzjzzTN9MstKKiorSkCFDNHLkyID7rVar3n33Xd13332Kjo4u9XlTU1MDdoD069dPL774YrFLiBTWrl07ffjhh6W+rx49euiBBx446THnn3++nn/++VKf8/LLL9eECRPUqFGjUt/GYrHonHPOKfXxpyMuLk4TJkzQ3//+d0VFRQV9nmeeeUajRo1SYmJiqW+TkJCg7t27B32foXTWWWfpyy+/VNu2bUt9G5PJpK5du5apzsGqVauWPv74Y9WrV6/EY2rWrKlx48apbt26xfaV9HfyxBNPnHLmLQAAQHkgc5SMzBEYmaN8kTkAAEAkIneUjNwRGLmjfJE7AACoHlg5EwBO4YEHHtDdd9+tlStX6rffftP69eu1c+dO7du3T7m5uXI4HIqLi1NKSopatGihHj16aNCgQapfv/5Jz2u1WvXggw/q5ptv1nfffaelS5dq48aNys7OVn5+vmJjY5WWlqamTZuqffv26tWrl3r06CGrNfBL9xVXXKEuXbpo/PjxWrBggf7880/Fx8erefPmGjRokK677roSb1uS++67T506ddInn3yi1atX69ixY0pJSVGHDh109dVXq3///lq6dGmZztmrVy/NnDlT8+bN05w5c7RmzRrt379fOTk5slgsSkpKUsOGDdWqVSv16NFD55xzjtLS0sp0H6cjOjpaTzzxhP7+979r6tSpWr58uTZu3KgjR47I6XQqNTVVtWrVUocOHdSnTx+1aNEi4HluuukmDR48WP/73/+0aNEi/f777zp06JDy8vIUExOjlJQUNWnSRGeeeabOOuss9erVS7GxsWGr56m0adNG33zzjRYvXqxZs2bpt99+0549e3Ts2DGZTCYlJiaqfv36atmypbp3765zzjnnpB0IodayZUtNnz5dEyZM0I8//qjMzExJUsOGDXX++efrr3/9q1JTU7V///5ity2pPZ155pmaOnWqPvzwQy1dulR79+6VzWYr13oAAABIZA4yB5mDzAEAAFD+yB3kDnIHuQMAAISfySi6hjsAIKK1bt262LaffvpJDRs2rIDSAOVn9erVuuaaa/y2mc1mLVq0SKmpqRVUKgAAgMhH5kB1QeYAAACoOOQOVBfkDgAAqjYuaw4AAKqcV155RbNnz5bdbg+4f+3atXrkkUeKbe/RowedFQAAAABOicwBAAAAoLyROwAAiHxc1hwAAFQ5ixcv1gcffKC4uDideeaZatSokeLi4pSbm6tNmzZp48aNxW5jMpn0j3/8owJKCwAAAKCqIXMAAAAAKG/kDgAAIh+DMwEAQJVls9m0cuVKrVy58pTHPvXUU+rcuXP5FwoAAABAxCBzAAAAAChv5A4AACIXgzMBAEBEa9CggUaPHq1+/fpVdFEAAAAARCAyBwAAAIDyRu4AAKBqYnAmAACocsaNG6fFixdr8eLFWr9+vfbu3avc3FxZLBYlJiaqfv36atu2rf7yl7/o3HPPlcViqegiAwAAAKhCyBwAAAAAyhu5AwCAyGcyDMOo6EIAAAAAAAAAAAAAAAAAAABECnNFFwAAAAAAAAAAAAAAAAAAACCSMDgTAAAAAAAAAAAAAAAAAP6/XTsWAAAAABjkbz2JncURwEjOBAAAAAAAAAAAABjJmQAAAAAAAAAAAAAjORMAAAAAAAAAAABgJGcCAAAAAAAAAAAAjORMAAAAAAAAAAAAgJGcCQAAAAAAAAAAADCSMwEAAAAAAAAAAABGciYAAAAAAAAAAADASM4EAAAAAAAAAAAAGMmZAAAAAAAAAAAAACM5EwAAAAAAAAAAAGAUTx48ckXXsdMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2700x900 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting infra, not important\n",
    "plt.style.use('seaborn-v0_8-whitegrid')\n",
    "plt.rcParams.update({\n",
    "    'font.family': 'sans-serif',\n",
    "    'font.size': 12,\n",
    "    'axes.labelsize': 14,\n",
    "    'axes.titlesize': 16,\n",
    "    'xtick.labelsize': 12,\n",
    "    'ytick.labelsize': 12,\n",
    "    'legend.fontsize': 12,\n",
    "    'figure.titlesize': 18\n",
    "})\n",
    "\n",
    "fig, axes = plt.subplots(1, len(batch_sizes), figsize=(18, 6), dpi=150)\n",
    "\n",
    "linear_color = '#1f77b4'\n",
    "softmax_color = '#ff7f0e'\n",
    "linear_marker = 'o'\n",
    "softmax_marker = 's'\n",
    "oom_marker = 'X'\n",
    "marker_size = 8\n",
    "line_width = 2.5\n",
    "\n",
    "legend_handles = []\n",
    "legend_labels = []\n",
    "\n",
    "for i, batch_size in enumerate(batch_sizes):\n",
    "    ax = axes[i]\n",
    "    seq_lengths = results[batch_size]['seq_lengths']\n",
    "    linear_times = results[batch_size]['linear_times']\n",
    "    softmax_times = results[batch_size]['softmax_times']\n",
    "    linear_oom = results[batch_size]['linear_oom']\n",
    "    softmax_oom = results[batch_size]['softmax_oom']\n",
    "    \n",
    "    valid_linear_indices = [i for i, oom in enumerate(linear_oom) if not oom]\n",
    "    valid_softmax_indices = [i for i, oom in enumerate(softmax_oom) if not oom]\n",
    "    \n",
    "    valid_linear_seqlens = [seq_lengths[i] for i in valid_linear_indices]\n",
    "    valid_linear_times = [linear_times[i] for i in valid_linear_indices]\n",
    "    \n",
    "    valid_softmax_seqlens = [seq_lengths[i] for i in valid_softmax_indices]\n",
    "    valid_softmax_times = [softmax_times[i] for i in valid_softmax_indices]\n",
    "    \n",
    "    line1, = ax.plot(valid_linear_seqlens, valid_linear_times, marker=linear_marker, \n",
    "            linestyle='-', linewidth=line_width, markersize=marker_size,\n",
    "            color=linear_color, label='Linear Attention', alpha=0.9)\n",
    "    \n",
    "    line2, = ax.plot(valid_softmax_seqlens, valid_softmax_times, marker=softmax_marker, \n",
    "            linestyle='-', linewidth=line_width, markersize=marker_size,\n",
    "            color=softmax_color, label='Softmax Attention', alpha=0.9)\n",
    "    \n",
    "    if i == 0:\n",
    "        legend_handles.append(line1)\n",
    "        legend_handles.append(line2)\n",
    "        legend_labels.append('Linear Attention')\n",
    "        legend_labels.append('Softmax Attention')\n",
    "    \n",
    "    oom_linear_indices = [i for i, oom in enumerate(linear_oom) if oom]\n",
    "    oom_softmax_indices = [i for i, oom in enumerate(softmax_oom) if oom]\n",
    "    \n",
    "    oom_linear_seqlens = [seq_lengths[i] for i in oom_linear_indices]\n",
    "    oom_softmax_seqlens = [seq_lengths[i] for i in oom_softmax_indices]\n",
    "    \n",
    "    ax.set_ylim(bottom=min([t for t in valid_linear_times + valid_softmax_times if t is not None]) * 0.5)\n",
    "    y_max = ax.get_ylim()[1] * 1.1\n",
    "    ax.set_ylim(top=y_max)\n",
    "    \n",
    "    if oom_linear_seqlens:\n",
    "        line3, = ax.plot(oom_linear_seqlens, [y_max * 0.95] * len(oom_linear_seqlens), \n",
    "                marker=oom_marker, linestyle='', markersize=marker_size+2, \n",
    "                color=linear_color, label='Linear Attention OOM', alpha=0.9)\n",
    "        if i == 0:\n",
    "            legend_handles.append(line3)\n",
    "            legend_labels.append('Linear Attention OOM')\n",
    "    \n",
    "    if oom_softmax_indices:\n",
    "        line4, = ax.plot(oom_softmax_seqlens, [y_max * 0.95] * len(oom_softmax_seqlens), \n",
    "                marker=oom_marker, linestyle='', markersize=marker_size+2, \n",
    "                color=softmax_color, label='Softmax Attention OOM', alpha=0.9)\n",
    "        if i == 0:\n",
    "            legend_handles.append(line4)\n",
    "            legend_labels.append('Softmax Attention OOM')\n",
    "    \n",
    "    ax.set_xscale('log', base=2)\n",
    "    ax.set_yscale('log')\n",
    "    ax.set_xlabel('Sequence Length', fontweight='bold')\n",
    "    \n",
    "    if i == 0:\n",
    "        ax.set_ylabel('Latency (seconds)', fontweight='bold')\n",
    "    \n",
    "    ax.set_title(f'Batch Size = {batch_size}', fontweight='bold')\n",
    "    \n",
    "    ax.grid(True, which='major', linestyle='-', alpha=0.3)\n",
    "    ax.grid(True, which='minor', linestyle=':', alpha=0.2)\n",
    "    \n",
    "    ax.set_xticks(seq_lengths)\n",
    "    ax.set_xticklabels([str(s) for s in seq_lengths], rotation=45)\n",
    "\n",
    "fig.legend(legend_handles, legend_labels, \n",
    "           loc='upper right', bbox_to_anchor=(0.99, 0.99),\n",
    "           frameon=True, fancybox=True, framealpha=0.95)\n",
    "\n",
    "fig.suptitle('Attention Mechanism Performance Comparison', fontweight='bold', y=1.05)\n",
    "\n",
    "# This is a BEAUTIFUL PLOT IMO \n",
    "# the plot shows a comparison of linear vs softmax attention performance across different sequence lengths and batch sizes\n",
    "# we can see that linear attention (blue) scales much better with sequence length than softmax attention (orange)\n",
    "# for batch size 1, linear attention maintains nearly constant latency while softmax grows quadratically\n",
    "# at larger batch sizes (64, 1024) the difference is less dramatic but linear attention still wins\n",
    "# the X markers indicate out-of-memory (OOM) errors - softmax hits OOM earlier due to quadratic memory usage\n",
    "# this empirically validates the theoretical O(N) vs O(N^2) complexity difference between the mechanisms\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.subplots_adjust(top=0.85, bottom=0.15, right=0.9)\n",
    "plt.show()\n"
   ]
  }
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